Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations7341
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory975.1 KiB
Average record size in memory136.0 B

Variable types

Categorical1
DateTime1
Numeric15

Alerts

Dataset has 4 (0.1%) duplicate rowsDuplicates
Balanca_Comercial is highly overall correlated with ExportacoesHigh correlation
Consumo is highly overall correlated with Gastos_Governo and 5 other fieldsHigh correlation
Exportacoes is highly overall correlated with Balanca_Comercial and 1 other fieldsHigh correlation
Gastos_Governo is highly overall correlated with Consumo and 4 other fieldsHigh correlation
Importacoes is highly overall correlated with Consumo and 1 other fieldsHigh correlation
Investimento is highly overall correlated with Consumo and 4 other fieldsHigh correlation
PIB is highly overall correlated with Consumo and 7 other fieldsHigh correlation
Producao_Industrial is highly overall correlated with Consumo and 4 other fieldsHigh correlation
Setor_Agricola is highly overall correlated with PIBHigh correlation
Setor_Servicos is highly overall correlated with Consumo and 4 other fieldsHigh correlation
Pais is uniformly distributed Uniform

Reproduction

Analysis started2025-09-22 13:56:36.522340
Analysis finished2025-09-22 13:57:12.815830
Duration36.29 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pais
Categorical

Uniform 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.5 KiB
Brasil
 
385
Indonésia
 
368
EUA
 
366
China
 
366
Alemanha
 
366
Other values (15)
5490 

Length

Max length13
Median length11
Mean length7.2472415
Min length3

Characters and Unicode

Total characters53202
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrasil
2nd rowBrasil
3rd rowBrasil
4th rowBrasil
5th rowBrasil

Common Values

ValueCountFrequency (%)
Brasil 385
 
5.2%
Indonésia 368
 
5.0%
EUA 366
 
5.0%
China 366
 
5.0%
Alemanha 366
 
5.0%
Japão 366
 
5.0%
Índia 366
 
5.0%
Reino Unido 366
 
5.0%
França 366
 
5.0%
Itália 366
 
5.0%
Other values (10) 3660
49.9%

Length

2025-09-22T10:57:12.958994image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sul 732
 
8.0%
do 732
 
8.0%
brasil 385
 
4.2%
indonésia 368
 
4.0%
alemanha 366
 
4.0%
china 366
 
4.0%
eua 366
 
4.0%
índia 366
 
4.0%
japão 366
 
4.0%
reino 366
 
4.0%
Other values (13) 4758
51.9%

Most occurring characters

ValueCountFrequency (%)
a 7341
 
13.8%
i 5511
 
10.4%
n 4396
 
8.3%
o 3296
 
6.2%
r 2581
 
4.9%
s 2217
 
4.2%
l 2215
 
4.2%
d 2198
 
4.1%
1830
 
3.4%
u 1830
 
3.4%
Other values (29) 19787
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7341
 
13.8%
i 5511
 
10.4%
n 4396
 
8.3%
o 3296
 
6.2%
r 2581
 
4.9%
s 2217
 
4.2%
l 2215
 
4.2%
d 2198
 
4.1%
1830
 
3.4%
u 1830
 
3.4%
Other values (29) 19787
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7341
 
13.8%
i 5511
 
10.4%
n 4396
 
8.3%
o 3296
 
6.2%
r 2581
 
4.9%
s 2217
 
4.2%
l 2215
 
4.2%
d 2198
 
4.1%
1830
 
3.4%
u 1830
 
3.4%
Other values (29) 19787
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7341
 
13.8%
i 5511
 
10.4%
n 4396
 
8.3%
o 3296
 
6.2%
r 2581
 
4.9%
s 2217
 
4.2%
l 2215
 
4.2%
d 2198
 
4.1%
1830
 
3.4%
u 1830
 
3.4%
Other values (29) 19787
37.2%

Data
Date

Distinct366
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size57.5 KiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-22T10:57:13.097748image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:13.279924image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PIB
Real number (ℝ)

High correlation 

Distinct7316
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50061.212
Minimum8422.66
Maximum85998.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:13.478818image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum8422.66
5-th percentile33315.01
Q143372.91
median50190.11
Q356875.63
95-th percentile66339.25
Maximum85998.12
Range77575.46
Interquartile range (IQR)13502.72

Descriptive statistics

Standard deviation10005.73
Coefficient of variation (CV)0.19986991
Kurtosis-0.039364187
Mean50061.212
Median Absolute Deviation (MAD)6737.7
Skewness-0.029875066
Sum3.6749935 × 108
Variance1.0011463 × 108
MonotonicityNot monotonic
2025-09-22T10:57:13.627760image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30199.16 18
 
0.2%
44700.43 3
 
< 0.1%
51881.41 2
 
< 0.1%
57083.18 2
 
< 0.1%
52428 2
 
< 0.1%
52832.56 2
 
< 0.1%
47255.09 2
 
< 0.1%
52606.77 2
 
< 0.1%
56811.3 1
 
< 0.1%
52906.48 1
 
< 0.1%
Other values (7306) 7306
99.5%
ValueCountFrequency (%)
8422.66 1
< 0.1%
10599.92 1
< 0.1%
17833.46 1
< 0.1%
18001.82 1
< 0.1%
18501.67 1
< 0.1%
19134.41 1
< 0.1%
19597.67 1
< 0.1%
19620.39 1
< 0.1%
19643.43 1
< 0.1%
21123.03 1
< 0.1%
ValueCountFrequency (%)
85998.12 1
< 0.1%
84647.14 1
< 0.1%
81722.47 1
< 0.1%
81197.94 1
< 0.1%
80919.92 1
< 0.1%
80668.91 1
< 0.1%
80376.82 1
< 0.1%
79281.65 1
< 0.1%
79009.75 1
< 0.1%
78055.82 1
< 0.1%

Consumo
Real number (ℝ)

High correlation 

Distinct7311
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30125.559
Minimum4897.24
Maximum54187.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:13.789372image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum4897.24
5-th percentile19326.53
Q125523.37
median29908.79
Q334649.65
95-th percentile41447.52
Maximum54187.33
Range49290.09
Interquartile range (IQR)9126.28

Descriptive statistics

Standard deviation6693.3625
Coefficient of variation (CV)0.22218218
Kurtosis-0.040987349
Mean30125.559
Median Absolute Deviation (MAD)4541.9
Skewness0.15555583
Sum2.2115173 × 108
Variance44801102
MonotonicityNot monotonic
2025-09-22T10:57:13.941878image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16960.01 18
 
0.2%
30318.9 3
 
< 0.1%
29867.27 2
 
< 0.1%
33488.68 2
 
< 0.1%
31772.86 2
 
< 0.1%
33504.09 2
 
< 0.1%
27228.75 2
 
< 0.1%
32542.98 2
 
< 0.1%
26749.21 2
 
< 0.1%
27480.41 2
 
< 0.1%
Other values (7301) 7304
99.5%
ValueCountFrequency (%)
4897.24 1
< 0.1%
6457.91 1
< 0.1%
9267.18 1
< 0.1%
9876.89 1
< 0.1%
10286 1
< 0.1%
10535.73 1
< 0.1%
10574.4 1
< 0.1%
10954.61 1
< 0.1%
11436.93 1
< 0.1%
11455.99 1
< 0.1%
ValueCountFrequency (%)
54187.33 1
< 0.1%
54169.29 1
< 0.1%
53387.72 1
< 0.1%
53041.2 1
< 0.1%
52725.6 1
< 0.1%
52446.47 1
< 0.1%
51925.92 1
< 0.1%
51650.34 1
< 0.1%
51511.59 1
< 0.1%
51283.91 1
< 0.1%

Investimento
Real number (ℝ)

High correlation 

Distinct7289
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7496.2606
Minimum1087.18
Maximum16973.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:14.089457image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1087.18
5-th percentile4401.97
Q15964.35
median7304.27
Q38867
95-th percentile11160.72
Maximum16973.58
Range15886.4
Interquartile range (IQR)2902.65

Descriptive statistics

Standard deviation2087.4795
Coefficient of variation (CV)0.27846944
Kurtosis-0.072550078
Mean7496.2606
Median Absolute Deviation (MAD)1447.53
Skewness0.41410804
Sum55030049
Variance4357570.5
MonotonicityNot monotonic
2025-09-22T10:57:14.235557image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4048.93 18
 
0.2%
7001.85 3
 
< 0.1%
5095.46 2
 
< 0.1%
5997.51 2
 
< 0.1%
5820.29 2
 
< 0.1%
8198.26 2
 
< 0.1%
6630.81 2
 
< 0.1%
7376.33 2
 
< 0.1%
7796.94 2
 
< 0.1%
9360.9 2
 
< 0.1%
Other values (7279) 7304
99.5%
ValueCountFrequency (%)
1087.18 1
< 0.1%
1319.81 1
< 0.1%
2082.56 1
< 0.1%
2177.02 1
< 0.1%
2285.41 1
< 0.1%
2392.21 1
< 0.1%
2447.06 1
< 0.1%
2481.16 1
< 0.1%
2599.06 1
< 0.1%
2602.68 1
< 0.1%
ValueCountFrequency (%)
16973.58 1
< 0.1%
14982.67 1
< 0.1%
14840.16 1
< 0.1%
14692.5 1
< 0.1%
14668.62 1
< 0.1%
14648.09 1
< 0.1%
14563.02 1
< 0.1%
14551.33 1
< 0.1%
14516.8 1
< 0.1%
14515.94 1
< 0.1%

Gastos_Governo
Real number (ℝ)

High correlation 

Distinct7285
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9991.2824
Minimum1430.28
Maximum18817.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:14.428324image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1430.28
5-th percentile6200.67
Q18210.92
median9867.22
Q311621.87
95-th percentile14342.49
Maximum18817.3
Range17387.02
Interquartile range (IQR)3410.95

Descriptive statistics

Standard deviation2484.8513
Coefficient of variation (CV)0.24870194
Kurtosis-0.11544567
Mean9991.2824
Median Absolute Deviation (MAD)1701.91
Skewness0.28485503
Sum73346004
Variance6174485.9
MonotonicityNot monotonic
2025-09-22T10:57:14.628250image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5507.81 18
 
0.2%
11058.2 3
 
< 0.1%
9886.4 2
 
< 0.1%
8021.19 2
 
< 0.1%
11475.61 2
 
< 0.1%
10713.55 2
 
< 0.1%
7927.72 2
 
< 0.1%
9320.94 2
 
< 0.1%
9369.44 2
 
< 0.1%
8301.78 2
 
< 0.1%
Other values (7275) 7304
99.5%
ValueCountFrequency (%)
1430.28 1
< 0.1%
1595.36 1
< 0.1%
3298.72 1
< 0.1%
3346.93 1
< 0.1%
3463.74 1
< 0.1%
3469.5 1
< 0.1%
3606.84 1
< 0.1%
3615.17 1
< 0.1%
3627.3 1
< 0.1%
3717.87 1
< 0.1%
ValueCountFrequency (%)
18817.3 1
< 0.1%
18738.46 1
< 0.1%
18420.1 1
< 0.1%
18273.46 1
< 0.1%
18262.61 1
< 0.1%
18256.52 1
< 0.1%
18161.74 1
< 0.1%
18132.44 1
< 0.1%
18114.04 1
< 0.1%
18056.92 1
< 0.1%

Exportacoes
Real number (ℝ)

High correlation 

Distinct7293
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9987.1584
Minimum863.67
Maximum22974.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:14.790278image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum863.67
5-th percentile4947.09
Q17158.35
median9602.04
Q312495.15
95-th percentile16307.17
Maximum22974.34
Range22110.67
Interquartile range (IQR)5336.8

Descriptive statistics

Standard deviation3550.1295
Coefficient of variation (CV)0.35546943
Kurtosis-0.41131349
Mean9987.1584
Median Absolute Deviation (MAD)2611.5
Skewness0.43008654
Sum73315730
Variance12603420
MonotonicityNot monotonic
2025-09-22T10:57:14.928011image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5393.09 18
 
0.2%
6854.51 3
 
< 0.1%
8011.87 2
 
< 0.1%
9846.57 2
 
< 0.1%
6442.74 2
 
< 0.1%
13074.13 2
 
< 0.1%
8562.23 2
 
< 0.1%
6938.48 2
 
< 0.1%
5083.17 2
 
< 0.1%
17813.54 2
 
< 0.1%
Other values (7283) 7304
99.5%
ValueCountFrequency (%)
863.67 1
< 0.1%
1725.91 1
< 0.1%
2429.64 1
< 0.1%
2698.7 1
< 0.1%
2709.58 1
< 0.1%
2765.34 1
< 0.1%
2820.88 1
< 0.1%
2883.52 1
< 0.1%
2916.78 1
< 0.1%
2920.4 1
< 0.1%
ValueCountFrequency (%)
22974.34 1
< 0.1%
22741.72 1
< 0.1%
22199.17 1
< 0.1%
22108.28 1
< 0.1%
21916.47 1
< 0.1%
21598.72 1
< 0.1%
21252.46 1
< 0.1%
21172.84 1
< 0.1%
21150.21 1
< 0.1%
20853.07 1
< 0.1%

Importacoes
Real number (ℝ)

High correlation 

Distinct7293
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8731.0558
Minimum1270.9
Maximum19969.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:15.072219image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1270.9
5-th percentile4583.79
Q16538.26
median8485.62
Q310708.97
95-th percentile13714.02
Maximum19969.82
Range18698.92
Interquartile range (IQR)4170.71

Descriptive statistics

Standard deviation2827.2315
Coefficient of variation (CV)0.32381324
Kurtosis-0.27008292
Mean8731.0558
Median Absolute Deviation (MAD)2060.57
Skewness0.40839337
Sum64094681
Variance7993238.1
MonotonicityNot monotonic
2025-09-22T10:57:15.221277image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3359.12 18
 
0.2%
5613.33 3
 
< 0.1%
7622.61 3
 
< 0.1%
7758.33 2
 
< 0.1%
8853.72 2
 
< 0.1%
9573.69 2
 
< 0.1%
6935.32 2
 
< 0.1%
8080.6 2
 
< 0.1%
5316.86 2
 
< 0.1%
12240.67 2
 
< 0.1%
Other values (7283) 7303
99.5%
ValueCountFrequency (%)
1270.9 1
< 0.1%
1574.65 1
< 0.1%
2201.23 1
< 0.1%
2576.97 1
< 0.1%
2697.64 1
< 0.1%
2740.7 1
< 0.1%
2803.97 1
< 0.1%
2840.84 1
< 0.1%
2884.36 1
< 0.1%
2906.43 1
< 0.1%
ValueCountFrequency (%)
19969.82 1
< 0.1%
18976.77 1
< 0.1%
18886.56 1
< 0.1%
18437.28 1
< 0.1%
18383.09 1
< 0.1%
18266.31 1
< 0.1%
18250.92 1
< 0.1%
18241.92 1
< 0.1%
18209.17 1
< 0.1%
17920.09 1
< 0.1%

Inflacao
Real number (ℝ)

Distinct101
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5050865
Minimum0
Maximum1
Zeros30
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:15.372270image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.26
median0.51
Q30.75
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.28752625
Coefficient of variation (CV)0.5692614
Kurtosis-1.1894173
Mean0.5050865
Median Absolute Deviation (MAD)0.25
Skewness-0.02148474
Sum3707.84
Variance0.082671345
MonotonicityNot monotonic
2025-09-22T10:57:15.524029image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87 104
 
1.4%
0.63 91
 
1.2%
0.15 89
 
1.2%
0.3 88
 
1.2%
0.52 87
 
1.2%
0.85 87
 
1.2%
0.44 86
 
1.2%
0.47 85
 
1.2%
0.71 85
 
1.2%
0.34 85
 
1.2%
Other values (91) 6454
87.9%
ValueCountFrequency (%)
0 30
 
0.4%
0.01 77
1.0%
0.02 65
0.9%
0.03 66
0.9%
0.04 68
0.9%
0.05 74
1.0%
0.06 78
1.1%
0.07 72
1.0%
0.08 74
1.0%
0.09 67
0.9%
ValueCountFrequency (%)
1 47
0.6%
0.99 60
0.8%
0.98 76
1.0%
0.97 77
1.0%
0.96 75
1.0%
0.95 77
1.0%
0.94 77
1.0%
0.93 73
1.0%
0.92 53
0.7%
0.91 78
1.1%

Desemprego
Real number (ℝ)

Distinct1199
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9988762
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:15.834883image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.61
Q15.93
median8.98
Q312.04
95-th percentile14.43
Maximum15
Range12
Interquartile range (IQR)6.11

Descriptive statistics

Standard deviation3.4979157
Coefficient of variation (CV)0.38870584
Kurtosis-1.2244136
Mean8.9988762
Median Absolute Deviation (MAD)3.06
Skewness0.004823673
Sum66060.75
Variance12.235414
MonotonicityNot monotonic
2025-09-22T10:57:16.004630image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.82 25
 
0.3%
5.57 16
 
0.2%
5.35 15
 
0.2%
10.57 14
 
0.2%
3.02 14
 
0.2%
6.24 14
 
0.2%
13.21 13
 
0.2%
4.85 13
 
0.2%
13.71 13
 
0.2%
14.69 13
 
0.2%
Other values (1189) 7191
98.0%
ValueCountFrequency (%)
3 5
 
0.1%
3.01 6
0.1%
3.02 14
0.2%
3.03 11
0.1%
3.04 5
 
0.1%
3.05 6
0.1%
3.06 9
0.1%
3.07 5
 
0.1%
3.08 6
0.1%
3.09 5
 
0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
14.99 6
0.1%
14.98 8
0.1%
14.97 9
0.1%
14.96 4
 
0.1%
14.95 11
0.1%
14.94 9
0.1%
14.93 9
0.1%
14.92 5
0.1%
14.91 3
 
< 0.1%

Taxa_Juros
Real number (ℝ)

Distinct1001
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9988421
Minimum0
Maximum10
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:16.140224image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.53
Q12.5
median4.97
Q37.48
95-th percentile9.5
Maximum10
Range10
Interquartile range (IQR)4.98

Descriptive statistics

Standard deviation2.8903447
Coefficient of variation (CV)0.57820283
Kurtosis-1.2018079
Mean4.9988421
Median Absolute Deviation (MAD)2.49
Skewness0.0095322029
Sum36696.5
Variance8.3540922
MonotonicityNot monotonic
2025-09-22T10:57:16.288995image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.62 25
 
0.3%
9.84 17
 
0.2%
8.62 16
 
0.2%
4.13 16
 
0.2%
4.4 15
 
0.2%
2.56 15
 
0.2%
8.95 15
 
0.2%
3.66 14
 
0.2%
3.48 14
 
0.2%
0.95 14
 
0.2%
Other values (991) 7180
97.8%
ValueCountFrequency (%)
0 4
 
0.1%
0.01 6
0.1%
0.02 6
0.1%
0.03 11
0.1%
0.04 6
0.1%
0.05 9
0.1%
0.06 5
0.1%
0.07 4
 
0.1%
0.08 4
 
0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9.99 10
0.1%
9.98 4
 
0.1%
9.97 6
0.1%
9.96 8
0.1%
9.95 11
0.1%
9.94 11
0.1%
9.93 8
0.1%
9.92 6
0.1%
9.91 8
0.1%

Divida_Publica
Real number (ℝ)

Distinct7319
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37504.932
Minimum8422.6
Maximum92356.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:16.435067image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum8422.6
5-th percentile15763.99
Q124918.73
median35965.27
Q348630.97
95-th percentile64531.18
Maximum92356.67
Range83934.07
Interquartile range (IQR)23712.24

Descriptive statistics

Standard deviation15338.101
Coefficient of variation (CV)0.40896224
Kurtosis-0.5253271
Mean37504.932
Median Absolute Deviation (MAD)11751.58
Skewness0.40630268
Sum2.753237 × 108
Variance2.3525734 × 108
MonotonicityNot monotonic
2025-09-22T10:57:16.571792image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11264.13 18
 
0.2%
17291.85 3
 
< 0.1%
43677.18 2
 
< 0.1%
52265.25 2
 
< 0.1%
21617.25 2
 
< 0.1%
62426.18 1
 
< 0.1%
19325.99 1
 
< 0.1%
29155.69 1
 
< 0.1%
31050.37 1
 
< 0.1%
51846.33 1
 
< 0.1%
Other values (7309) 7309
99.6%
ValueCountFrequency (%)
8422.6 1
< 0.1%
8729.44 1
< 0.1%
8756 1
< 0.1%
8957.43 1
< 0.1%
9031.66 1
< 0.1%
9050.97 1
< 0.1%
9280.92 1
< 0.1%
9309.14 1
< 0.1%
9337.78 1
< 0.1%
9430.3 1
< 0.1%
ValueCountFrequency (%)
92356.67 1
< 0.1%
92283.17 1
< 0.1%
90435.04 1
< 0.1%
90198.11 1
< 0.1%
88428.69 1
< 0.1%
87781.02 1
< 0.1%
86275.52 1
< 0.1%
85779.81 1
< 0.1%
85759.49 1
< 0.1%
85227.91 1
< 0.1%

Producao_Industrial
Real number (ℝ)

High correlation 

Distinct7299
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13784.155
Minimum1830.61
Maximum29052.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:16.706613image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum1830.61
5-th percentile8380.94
Q111253.68
median13499.96
Q316112.52
95-th percentile20074.36
Maximum29052.28
Range27221.67
Interquartile range (IQR)4858.84

Descriptive statistics

Standard deviation3554.8799
Coefficient of variation (CV)0.2578961
Kurtosis-0.027515545
Mean13784.155
Median Absolute Deviation (MAD)2407.46
Skewness0.34591178
Sum1.0118949 × 108
Variance12637171
MonotonicityNot monotonic
2025-09-22T10:57:16.849255image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6462.05 18
 
0.2%
15445.67 3
 
< 0.1%
15604.35 2
 
< 0.1%
12782.95 2
 
< 0.1%
15967.93 2
 
< 0.1%
13478.37 2
 
< 0.1%
12395.7 2
 
< 0.1%
10906.19 2
 
< 0.1%
13475.5 2
 
< 0.1%
18844.47 2
 
< 0.1%
Other values (7289) 7304
99.5%
ValueCountFrequency (%)
1830.61 1
< 0.1%
3060.63 1
< 0.1%
3965.76 1
< 0.1%
4191.54 1
< 0.1%
4286.39 1
< 0.1%
4583.71 1
< 0.1%
4905.97 1
< 0.1%
4959.16 1
< 0.1%
5085.73 1
< 0.1%
5091.82 1
< 0.1%
ValueCountFrequency (%)
29052.28 1
< 0.1%
27141.24 1
< 0.1%
26210.78 1
< 0.1%
26128.34 1
< 0.1%
25891.44 1
< 0.1%
25530.73 1
< 0.1%
25500.92 1
< 0.1%
25462.26 1
< 0.1%
25425.06 1
< 0.1%
25177.19 1
< 0.1%

Setor_Agricola
Real number (ℝ)

High correlation 

Distinct7283
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4973.9071
Minimum490.87
Maximum11443.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:16.988340image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum490.87
5-th percentile2460.32
Q13565.59
median4762.76
Q36201.7
95-th percentile8231.06
Maximum11443.89
Range10953.02
Interquartile range (IQR)2636.11

Descriptive statistics

Standard deviation1786.2102
Coefficient of variation (CV)0.35911611
Kurtosis-0.36319812
Mean4973.9071
Median Absolute Deviation (MAD)1318.99
Skewness0.47386752
Sum36513452
Variance3190546.8
MonotonicityNot monotonic
2025-09-22T10:57:17.117620image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3156.13 18
 
0.2%
4729.56 3
 
< 0.1%
8149.64 2
 
< 0.1%
5535.46 2
 
< 0.1%
6200.59 2
 
< 0.1%
3790.92 2
 
< 0.1%
4976.22 2
 
< 0.1%
4601.36 2
 
< 0.1%
4354.45 2
 
< 0.1%
7261.59 2
 
< 0.1%
Other values (7273) 7304
99.5%
ValueCountFrequency (%)
490.87 1
< 0.1%
883.28 1
< 0.1%
1165.33 1
< 0.1%
1234.34 1
< 0.1%
1262 1
< 0.1%
1390.16 1
< 0.1%
1443.28 1
< 0.1%
1443.96 1
< 0.1%
1518.04 1
< 0.1%
1518.24 1
< 0.1%
ValueCountFrequency (%)
11443.89 1
< 0.1%
11421.98 1
< 0.1%
10981.31 1
< 0.1%
10927.87 1
< 0.1%
10852.83 1
< 0.1%
10770.15 1
< 0.1%
10741.56 1
< 0.1%
10739.59 1
< 0.1%
10687.38 1
< 0.1%
10629.09 1
< 0.1%

Setor_Servicos
Real number (ℝ)

High correlation 

Distinct7311
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25051.142
Minimum4695.95
Maximum45572.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-09-22T10:57:17.237394image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum4695.95
5-th percentile15854.72
Q121003.51
median24776.04
Q328884.03
95-th percentile35157.82
Maximum45572.33
Range40876.38
Interquartile range (IQR)7880.52

Descriptive statistics

Standard deviation5874.4432
Coefficient of variation (CV)0.23449802
Kurtosis-0.019284353
Mean25051.142
Median Absolute Deviation (MAD)3921.88
Skewness0.23577408
Sum1.8390043 × 108
Variance34509083
MonotonicityNot monotonic
2025-09-22T10:57:17.378053image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12300.6 18
 
0.2%
25486.37 3
 
< 0.1%
20917.63 2
 
< 0.1%
25335.95 2
 
< 0.1%
19926.35 2
 
< 0.1%
27423.53 2
 
< 0.1%
23266.76 2
 
< 0.1%
26534.73 2
 
< 0.1%
23813.33 2
 
< 0.1%
20382.54 2
 
< 0.1%
Other values (7301) 7304
99.5%
ValueCountFrequency (%)
4695.95 1
< 0.1%
5423.14 1
< 0.1%
7498.92 1
< 0.1%
7926.03 1
< 0.1%
8972.01 1
< 0.1%
9213.43 1
< 0.1%
9739.9 1
< 0.1%
9860.82 1
< 0.1%
9873.59 1
< 0.1%
9899.5 1
< 0.1%
ValueCountFrequency (%)
45572.33 1
< 0.1%
45201 1
< 0.1%
44576.48 1
< 0.1%
44358.4 1
< 0.1%
44052.76 1
< 0.1%
43952.46 1
< 0.1%
43895.46 1
< 0.1%
43853.37 1
< 0.1%
43815.02 1
< 0.1%
43813.31 1
< 0.1%

Balanca_Comercial
Real number (ℝ)

High correlation 

Distinct7298
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.1026
Minimum-10739.44
Maximum14100.35
Zeros0
Zeros (%)0.0%
Negative2752
Negative (%)37.5%
Memory size57.5 KiB
2025-09-22T10:57:17.522971image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum-10739.44
5-th percentile-4769.05
Q1-1291.72
median1212.82
Q33756.54
95-th percentile7432.77
Maximum14100.35
Range24839.79
Interquartile range (IQR)5048.26

Descriptive statistics

Standard deviation3684.6422
Coefficient of variation (CV)2.9333927
Kurtosis-0.30513647
Mean1256.1026
Median Absolute Deviation (MAD)2527.66
Skewness0.081731245
Sum9221049.1
Variance13576588
MonotonicityNot monotonic
2025-09-22T10:57:17.667718image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2033.97 18
 
0.2%
1241.18 3
 
< 0.1%
-684.12 2
 
< 0.1%
-233.69 2
 
< 0.1%
-1291.72 2
 
< 0.1%
1671.74 2
 
< 0.1%
-397.41 2
 
< 0.1%
1963.19 2
 
< 0.1%
644.34 2
 
< 0.1%
3722.62 2
 
< 0.1%
Other values (7288) 7304
99.5%
ValueCountFrequency (%)
-10739.44 1
< 0.1%
-10019.95 1
< 0.1%
-9612.09 1
< 0.1%
-9513.46 1
< 0.1%
-9499.63 1
< 0.1%
-9096.91 1
< 0.1%
-8851.33 1
< 0.1%
-8836.08 1
< 0.1%
-8807.31 1
< 0.1%
-8668.95 1
< 0.1%
ValueCountFrequency (%)
14100.35 1
< 0.1%
12493.62 1
< 0.1%
12211.06 1
< 0.1%
12095.09 1
< 0.1%
12080 1
< 0.1%
12012.43 1
< 0.1%
11983.24 1
< 0.1%
11842.74 1
< 0.1%
11715.15 1
< 0.1%
11687.18 1
< 0.1%

Crescimento_PIB
Real number (ℝ)

Distinct301
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50400354
Minimum-1
Maximum2
Zeros16
Zeros (%)0.2%
Negative2431
Negative (%)33.1%
Memory size57.5 KiB
2025-09-22T10:57:17.808191image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.84
Q1-0.25
median0.5
Q31.24
95-th percentile1.85
Maximum2
Range3
Interquartile range (IQR)1.49

Descriptive statistics

Standard deviation0.85860742
Coefficient of variation (CV)1.7035742
Kurtosis-1.1950066
Mean0.50400354
Median Absolute Deviation (MAD)0.75
Skewness0.0027149244
Sum3699.89
Variance0.73720671
MonotonicityNot monotonic
2025-09-22T10:57:17.947530image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.17 49
 
0.7%
1.41 39
 
0.5%
-0.25 38
 
0.5%
-0.58 36
 
0.5%
1.31 35
 
0.5%
-0.39 35
 
0.5%
1.71 35
 
0.5%
-0.29 34
 
0.5%
1.06 34
 
0.5%
0.08 34
 
0.5%
Other values (291) 6972
95.0%
ValueCountFrequency (%)
-1 5
 
0.1%
-0.99 21
0.3%
-0.98 22
0.3%
-0.97 13
 
0.2%
-0.96 31
0.4%
-0.95 33
0.4%
-0.94 27
0.4%
-0.93 25
0.3%
-0.92 16
0.2%
-0.91 22
0.3%
ValueCountFrequency (%)
2 13
0.2%
1.99 17
0.2%
1.98 21
0.3%
1.97 15
0.2%
1.96 19
0.3%
1.95 28
0.4%
1.94 28
0.4%
1.93 24
0.3%
1.92 30
0.4%
1.91 27
0.4%

Interactions

2025-09-22T10:57:09.641458image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:37.663600image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.051003image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.835595image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.297975image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.762619image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.755391image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.118571image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.531682image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.718678image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.869140image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.208991image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.460661image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.673591image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.332723image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.793437image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:37.845590image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.204218image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.002082image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.448881image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.871527image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.921380image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.291099image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.670761image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.876747image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.008553image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.355178image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.599087image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.835197image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.502172image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.960674image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.036627image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.332000image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.190686image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.585740image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.958721image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.070459image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.429368image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.797651image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.010756image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.149505image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.520678image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.751648image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.980980image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.643717image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.145712image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.177579image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.474767image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.348710image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.751949image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.091194image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.225602image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.584361image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.947874image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.156314image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.297764image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.675891image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.903043image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:05.178790image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.795730image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.318568image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.317177image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.579355image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.512690image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.900377image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.210982image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.377856image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.712682image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.095187image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.307979image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.482098image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.816452image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.052425image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:05.347919image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.930342image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.497098image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.447292image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.660378image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.649823image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:45.044373image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.368888image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.531250image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:51.861995image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.228446image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.438655image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.623979image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.967759image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.197485image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:05.500758image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.090397image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.671235image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.587476image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.776850image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.811746image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:45.218004image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.499869image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.685774image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.009501image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.374085image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.577783image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:58.938121image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.120604image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.360763image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:05.695517image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.287761image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.806717image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.720645image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.865443image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:42.979244image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:45.363217image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.633920image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:49.872128image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.358325image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.518749image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.717859image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.077140image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.275261image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.502505image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.027772image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.466365image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:10.967721image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:38.859369image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:40.957840image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.153361image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:45.507974image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.764352image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.019313image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.497607image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.655740image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.866729image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.207857image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.413983image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.653417image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.184010image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.593507image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.112003image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.009352image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.064221image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.311306image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:45.656530image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:47.907831image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.166906image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.637104image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.799373image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:56.997719image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.349807image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.567001image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.787644image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.334565image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.759895image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.257699image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.137885image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.176261image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.459498image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.002222image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.041708image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.320093image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.767655image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:54.927756image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.128345image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.479926image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.703432image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:03.918933image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.483417image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:08.898060image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.413435image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.287047image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.276155image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.601478image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.150363image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.179869image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.472285image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:52.917891image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.083085image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.283419image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.624393image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.846554image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.071364image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.677909image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.059683image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.559252image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.427145image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.395318image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.770606image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.305017image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.315038image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.627954image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.065646image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.217737image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.420926image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.753545image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:01.988724image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.227644image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.826743image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.198320image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.750950image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.593374image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.563057image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:43.948964image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.480015image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.480162image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.808275image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.220943image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.402008image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.598058image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:59.905638image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.150505image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.381078image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:06.995102image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.357571image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:11.894386image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:39.742007image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:41.699107image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:44.102596image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:46.612049image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:48.618812image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:50.967745image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:53.374954image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:55.540682image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:56:57.726109image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:00.067811image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:02.300881image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:04.517778image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:07.173698image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
2025-09-22T10:57:09.497205image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/

Correlations

2025-09-22T10:57:18.076403image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Balanca_ComercialConsumoCrescimento_PIBDesempregoDivida_PublicaExportacoesGastos_GovernoImportacoesInflacaoInvestimentoPIBPaisProducao_IndustrialSetor_AgricolaSetor_ServicosTaxa_Juros
Balanca_Comercial1.0000.0540.0170.0150.0330.6980.051-0.4220.0140.0430.0710.0000.0680.0400.0680.015
Consumo0.0541.000-0.0040.0020.4070.4760.7060.526-0.0210.6160.8900.0080.6860.4660.763-0.000
Crescimento_PIB0.017-0.0041.0000.002-0.0060.0020.003-0.021-0.001-0.0090.0050.000-0.0010.001-0.0030.003
Desemprego0.0150.0020.0021.000-0.0000.012-0.009-0.005-0.0070.003-0.0020.0200.0170.019-0.007-0.004
Divida_Publica0.0330.407-0.006-0.0001.0000.2600.3670.281-0.0120.3170.4510.0260.3540.2460.394-0.003
Exportacoes0.6980.4760.0020.0120.2601.0000.4270.3190.0020.3760.5360.0230.4280.2920.4680.015
Gastos_Governo0.0510.7060.003-0.0090.3670.4271.0000.472-0.0170.5550.7890.0000.6200.4160.678-0.005
Importacoes-0.4220.526-0.021-0.0050.2810.3190.4721.000-0.0180.4150.5830.0090.4470.3100.499-0.001
Inflacao0.014-0.021-0.001-0.007-0.0120.002-0.017-0.0181.000-0.010-0.0180.028-0.019-0.006-0.016-0.003
Investimento0.0430.616-0.0090.0030.3170.3760.5550.415-0.0101.0000.6860.0180.5280.3680.585-0.006
PIB0.0710.8900.005-0.0020.4510.5360.7890.583-0.0180.6861.0000.0170.7630.5220.8490.001
Pais0.0000.0080.0000.0200.0260.0230.0000.0090.0280.0180.0171.0000.0230.0150.0310.007
Producao_Industrial0.0680.686-0.0010.0170.3540.4280.6200.447-0.0190.5280.7630.0231.0000.3970.653-0.008
Setor_Agricola0.0400.4660.0010.0190.2460.2920.4160.310-0.0060.3680.5220.0150.3971.0000.444-0.004
Setor_Servicos0.0680.763-0.003-0.0070.3940.4680.6780.499-0.0160.5850.8490.0310.6530.4441.000-0.010
Taxa_Juros0.015-0.0000.003-0.004-0.0030.015-0.005-0.001-0.003-0.0060.0010.007-0.008-0.004-0.0101.000

Missing values

2025-09-22T10:57:12.142681image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-22T10:57:12.557546image/svg+xmlMatplotlib v3.10.6, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PaisDataPIBConsumoInvestimentoGastos_GovernoExportacoesImportacoesInflacaoDesempregoTaxa_JurosDivida_PublicaProducao_IndustrialSetor_AgricolaSetor_ServicosBalanca_ComercialCrescimento_PIB
0Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
1Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
2Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
3Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
4Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
5Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
6Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
7Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
8Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
9Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.62033.971.17
PaisDataPIBConsumoInvestimentoGastos_GovernoExportacoesImportacoesInflacaoDesempregoTaxa_JurosDivida_PublicaProducao_IndustrialSetor_AgricolaSetor_ServicosBalanca_ComercialCrescimento_PIB
7331Egito22/12/202447631.1828778.969267.508283.0713992.847086.900.615.269.7333972.6310300.026597.4423165.576905.951.05
7332Egito23/12/202438571.7419810.677277.106635.057032.324201.600.954.526.1636056.969458.674586.5020754.792830.720.81
7333Egito24/12/202439950.8724790.064222.396703.6210879.034279.060.298.666.3845391.3213167.464035.0420449.286599.971.23
7334Egito25/12/202447894.1628897.668352.5711121.628066.896024.740.307.649.3017114.7815655.964934.0420156.742042.15-0.37
7335Egito26/12/202444778.6531146.954905.156781.308211.764551.510.107.596.0931036.1111256.625344.6222499.553660.25-0.54
7336Egito27/12/202433317.5619399.756474.346832.615416.334537.430.3012.545.8115595.977550.882178.0118611.13878.891.36
7337Egito28/12/202441022.0627008.047659.607147.086239.876358.850.803.828.4141755.1110553.733118.3118726.71-118.991.11
7338Egito29/12/202437722.6523167.075645.408499.786230.147857.740.574.199.2039090.498457.502229.2917242.90-1627.60-0.88
7339Egito30/12/202452429.9935854.466576.098112.086447.666126.540.014.812.5143330.9918269.837208.3723889.51321.121.79
7340Egito31/12/202453644.9836670.068651.0311859.676872.7610535.300.887.874.2816778.8917298.075918.1130232.94-3662.54-0.06

Duplicate rows

Most frequently occurring

PaisDataPIBConsumoInvestimentoGastos_GovernoExportacoesImportacoesInflacaoDesempregoTaxa_JurosDivida_PublicaProducao_IndustrialSetor_AgricolaSetor_ServicosBalanca_ComercialCrescimento_PIB# duplicates
0Brasil01/01/202430199.1616960.014048.935507.815393.093359.120.639.824.6211264.136462.053156.1312300.602033.971.1718
3Indonésia14/10/202444700.4330318.907001.8511058.206854.515613.330.538.114.7717291.8515445.674729.5625486.371241.181.053
1Brasil25/01/202452606.7733504.095578.4111475.616938.487622.610.744.965.3243677.1811818.304075.8827423.53-684.120.382
2Brasil30/01/202447255.0927228.758008.438925.665083.175316.860.4414.881.7852265.2515967.934298.5523266.76-233.690.512